Lexicon Generation for Emotion Detection from Text

Bandhakavi, Anil; Wiratunga, Nirmalie; Massie, Stewart; Deepak, P

doi:10.1109/mis.2017.22

Cited by 82 publications

(40 citation statements)

References 12 publications

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“…Incorporating Knowledge in Sentiment Analysis: Traditional lexicon-based methods detect emotions or sentiments from a piece of text based on the emotions or sentiments of words or phrases that compose it (Hu et al, 2009;Taboada et al, 2011;Bandhakavi et al, 2017). Few studies investigated the usage of knowledge bases in deep learning methods.…”

Section: Knowledge Base In Conversationsmentioning

confidence: 99%

Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations

Zhong¹,

Wang²,

Chen³

2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

212

132

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Messages in human conversations inherently convey emotions. The task of detecting emotions in textual conversations leads to a wide range of applications such as opinion mining in social networks. However, enabling machines to analyze emotions in conversations is challenging, partly because humans often rely on the context and commonsense knowledge to express emotions. In this paper, we address these challenges by proposing a Knowledge-Enriched Transformer (KET), where contextual utterances are interpreted using hierarchical self-attention and external commonsense knowledge is dynamically leveraged using a context-aware affective graph attention mechanism. Experiments on multiple textual conversation datasets demonstrate that both context and commonsense knowledge are consistently beneficial to the emotion detection performance. In addition, the experimental results show that our KET model outperforms the state-of-the-art models on most of the tested datasets in F1 score.

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Section: Knowledge Base In Conversationsmentioning

confidence: 99%

Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations

Zhong¹,

Wang²,

Chen³

2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

212

132

View full text Add to dashboard Cite

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“…Sentiment analysis techniques can be broadly categorized into symbolic and sub-symbolic approaches: the former include the use of lexicons [16], ontologies [17], and semantic networks [18] to encode the polarity associated with words and multi-word expressions; the latter consist of supervised [19], semisupervised [20] and unsupervised [21] machine learning techniques, that perform sentiment classification based on word co-occurrence frequencies. Among these, the most popular algorithms are based on deep neural networks [22] and generative adversarial networks [23].…”

Section: English Sentiment Analysis Approachesmentioning

confidence: 99%

A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks

et al. 2020

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Social media hold valuable, vast and unstructured information on public opinion that can be utilized to improve products and services. The automatic analysis of such data, however, requires a deep understanding of natural language.Current sentiment analysis approaches are mainly based on word co-occurrence frequencies, which are inadequate in most practical cases. In this work, we propose a novel hybrid framework for concept-level sentiment analysis in Persian language, that integrates linguistic rules and deep learning to optimize polarity detection. When a pattern is triggered, the framework allows sentiments to flow from words to concepts based on symbolic dependency relations. When no pattern is triggered, the framework switches to its subsymbolic counterpart and leverages deep neural networks (DNN) to perform the classification. The proposed framework outperforms state-of-the-art approaches (including support vector machine, and logistic regression) and DNN classifiers (long short-term memory, and Convolutional Neural Networks) with a margin of 10-15% and 3-4% respectively, using benchmark Persian product and hotel reviews corpora.

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“…In the following section we briefly explain our proposed method to generate Sentilex and Emolex. Further details about our proposed method can be found in [1,2] 4 Mixture Model for Lexicon Generation…”

Section: Emotion Corpus-sentilexmentioning

confidence: 99%

“…Our contributions in this paper are as follows: 1. We propose two different methods to generate sentiment lexicons from a corpus of emotion-labelled tweets by combining our prior work on domain-specific emotion lexicon generation [1,2], with the emotion-sentiment mapping presented in Psychology (see figure 1) [4]; and 2. We comparatively evaluate the quality of the proposed sentiment lexicons, and the standard sentiment lexicons found in literature through different sentiment analysis tasks: sentiment intensity prediction and sentiment classification on benchmark Twitter data sets.…”

Section: Introductionmentioning

confidence: 99%